The main purpose of our approach is to provide a way to transfer a model trained on labeled data from the source domain to the unlabeled target domain.
Generally, the model with good generalization can reduce the discrepancy of intermediate states generated from different distributions [1]}. We use the BERT model [2]}, which is a pre-trained contextual model based on unsupervised NLP tasks with a huge 3.3-billion-word corpus. Its model depth and huge training data size ensure that it can generate universal feature representations under a variety of linguistic conditions. And we consider applying adversarial learning to minimize cross-domain discrepancy between \(\mathcal {D}({x},{y})\)  and \(\mathcal {D}^{\prime }({x^{\prime }},{y^{\prime }})\)  [3]}. Moreover, pseudo-label based self-training [4]} with low-confidence filtering is also utilized for further leveraging unlabeled data in the target domain.
